A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.
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2. The system of claim 1, wherein the first bounding box operation comprises utilizing the neural network to generate convolution layer heatmaps of the object in the one or more images.
This invention relates to computer vision systems for object detection and localization in images. The system addresses the challenge of accurately identifying and bounding objects within digital images, particularly in complex scenes with overlapping or occluded objects. The system employs a neural network to process input images and generate convolution layer heatmaps, which highlight regions of interest where objects are likely present. These heatmaps are then used to define initial bounding boxes around detected objects. The system further refines these bounding boxes through additional processing steps to improve accuracy. The neural network is trained to recognize specific object classes and their spatial relationships within the image, enabling precise localization. The heatmaps provide a probabilistic representation of object presence, allowing the system to handle variations in object appearance, scale, and orientation. This approach enhances detection performance in real-world applications such as autonomous driving, surveillance, and medical imaging, where reliable object localization is critical. The system integrates convolutional neural network (CNN) architectures to extract hierarchical features from the input images, ensuring robust detection across diverse scenarios. The heatmap-based bounding box generation improves upon traditional methods by leveraging deep learning techniques to capture fine-grained spatial information. This invention contributes to advancements in automated object detection by combining neural network-based feature extraction with geometric bounding box refinement.
6. The system of claim 1, wherein in response to the feedback being the first bounding box representing the object in the one or more images, outputting the first set of annotation data.
The system relates to image processing and object detection, specifically addressing the challenge of accurately identifying and annotating objects within images. The system processes one or more images to detect objects and generates bounding boxes around them. When user feedback indicates that a first bounding box correctly represents an object in the images, the system outputs a first set of annotation data. This annotation data may include metadata, labels, or other descriptive information about the detected object. The system may also compare the feedback against predefined criteria or machine learning models to validate the accuracy of the bounding box. If the feedback confirms the bounding box is correct, the system proceeds to generate and output the annotation data, which can be used for further analysis, training datasets, or automated workflows. The system may also handle cases where the feedback indicates the bounding box is incorrect, triggering adjustments or additional processing steps. The overall goal is to improve the reliability and efficiency of object detection and annotation in image processing applications.
7. The system of claim 1, wherein the neural network based object localization framework is trained using an unsupervised learning operation with unannotated image data for a plurality of objects.
The system relates to object localization in images using a neural network-based framework. Traditional object localization methods rely on supervised learning, requiring large amounts of annotated training data, which is time-consuming and expensive to obtain. This system addresses the problem by employing an unsupervised learning approach, eliminating the need for manual annotations. The neural network framework is trained using unannotated image data, allowing it to learn object locations and features without labeled examples. The system processes input images to detect and localize multiple objects within the scene, improving efficiency and scalability. By leveraging unsupervised learning, the framework reduces dependency on labeled datasets, making it more adaptable to real-world applications where annotated data is scarce. The training process involves exposing the neural network to a diverse set of unannotated images, enabling it to identify patterns and spatial relationships between objects autonomously. This approach enhances accuracy and robustness in object detection tasks, particularly in scenarios with limited labeled data. The system is designed to operate across various domains, including autonomous vehicles, surveillance, and medical imaging, where rapid and accurate object localization is critical. The unsupervised training method ensures the framework can generalize well to new, unseen data, improving its practical utility.
9. The method of claim 8, wherein the first bounding box operation comprises utilizing the neural network to generate convolution layer heatmaps of the object in the one or more images.
The invention relates to object detection in images using neural networks, specifically improving the accuracy of bounding box operations. The problem addressed is the difficulty in precisely identifying and localizing objects within images, which is critical for applications like autonomous vehicles, surveillance, and medical imaging. The method involves using a neural network to generate convolution layer heatmaps of an object in one or more images. These heatmaps highlight regions of interest by emphasizing areas where the object is likely present, enhancing the detection process. The neural network processes the input images through convolutional layers, producing feature maps that are then converted into heatmaps. These heatmaps are used to refine the initial bounding box predictions, ensuring more accurate object localization. The method may also include additional bounding box operations, such as non-maximum suppression, to eliminate redundant detections and improve overall detection performance. The approach leverages deep learning techniques to enhance the precision of object detection, making it suitable for real-world applications requiring high accuracy.
13. The method of claim 8, wherein in response to the first bounding box representing the object in the one or more images, outputting the first set of annotation data.
A system and method for object detection and annotation in images involves identifying objects within one or more images and generating annotation data to describe those objects. The method includes processing the images to detect objects and determining a first bounding box that represents the spatial location of an object within the image. The bounding box defines the object's boundaries, allowing for precise localization. Once the bounding box is generated, the system outputs a first set of annotation data associated with the object. This annotation data may include metadata such as object class, confidence scores, or other descriptive attributes. The method ensures that detected objects are accurately represented and annotated, facilitating further analysis or machine learning tasks. The approach improves object detection accuracy and provides structured data for downstream applications, addressing challenges in automated image analysis where precise object localization and annotation are required. The system may also support multiple bounding boxes for overlapping or complex objects, enhancing flexibility in various imaging scenarios.
14. The method of claim 8, wherein the neural network based object localization framework is trained using an unsupervised learning operation with unannotated image data for a plurality of objects.
The invention relates to a neural network-based object localization framework that improves training efficiency by using unsupervised learning with unannotated image data. Traditional object localization methods rely on supervised learning, requiring extensive labeled datasets, which are time-consuming and costly to produce. This invention addresses this limitation by enabling the neural network to learn object locations from unlabeled images, reducing dependency on manual annotation. The framework processes a plurality of objects within images without prior annotations, leveraging unsupervised learning techniques to identify and localize objects based on inherent patterns in the data. This approach allows the system to generalize better across diverse datasets and adapt to new environments with minimal human intervention. The method includes feature extraction, clustering, and spatial analysis to determine object boundaries and positions without explicit labels. By eliminating the need for annotated training data, the invention significantly lowers the barrier to deploying object localization systems in real-world applications, such as autonomous vehicles, surveillance, and robotics, where large-scale labeled datasets are impractical. The framework maintains accuracy while reducing training costs and time, making it scalable for large-scale deployments. The unsupervised learning approach also enhances adaptability, allowing the system to continuously improve as it processes more unannotated data.
16. The non-transitory machine-readable medium of claim 15, wherein the first bounding box operation comprises utilizing the neural network to generate convolution layer heatmaps of the object in the one or more images.
The invention relates to computer vision systems that analyze images to detect and localize objects. A common challenge in such systems is accurately identifying objects within images, particularly when objects vary in size, orientation, or appearance. The invention addresses this by using a neural network to generate convolution layer heatmaps, which highlight regions of interest in the image where an object is likely present. These heatmaps are then used to define a bounding box around the detected object, improving localization accuracy. The neural network processes input images through convolutional layers, producing feature maps that emphasize key object characteristics. The heatmaps are derived from these feature maps, with higher intensity values indicating stronger object presence. The bounding box operation refines these heatmaps to precisely outline the object's boundaries. This approach enhances object detection by leveraging deep learning techniques to capture spatial relationships and fine-grained details in the image. The system is particularly useful in applications like autonomous vehicles, surveillance, and medical imaging, where precise object localization is critical. The invention improves upon prior methods by using convolutional neural networks to generate heatmaps, which provide a more robust and adaptable way to handle variations in object appearance and context.
20. The non-transitory machine-readable medium of claim 15, wherein in response to the feedback being the first bounding box representing the object in the one or more images, outputting the first set of annotation data.
This invention relates to computer vision systems for object detection and annotation in images. The problem addressed is the need for accurate and efficient annotation of objects within images, particularly in automated or semi-automated systems where user feedback is used to refine detection results. The system processes one or more images to identify objects and generates annotation data, such as bounding boxes, to mark the detected objects. The system includes a feedback mechanism where a user can provide input, such as selecting a bounding box that correctly represents an object in the image. In response to this feedback, the system outputs a set of annotation data corresponding to the user-selected bounding box. This annotation data can include coordinates, labels, or other metadata describing the object's location and characteristics within the image. The system may also include a machine learning model trained to detect objects in images, where the feedback is used to improve the model's accuracy over time. The feedback mechanism allows the system to dynamically adjust its output based on user corrections, ensuring that the annotation data remains precise and reliable. This approach is particularly useful in applications like autonomous driving, medical imaging, or industrial inspection, where accurate object detection is critical. The invention improves upon prior systems by integrating real-time user feedback to enhance annotation quality without requiring extensive manual intervention.
21. The non-transitory machine-readable medium of claim 15, wherein the neural network based object localization framework is trained using an unsupervised learning operation with unannotated image data for a plurality of objects.
This invention relates to a machine-readable medium storing a neural network-based object localization framework trained using unsupervised learning techniques. The framework is designed to identify and locate objects within images without relying on pre-annotated training data. Traditional object localization methods often require extensive labeled datasets, which are time-consuming and costly to produce. This invention addresses this challenge by leveraging unsupervised learning, where the neural network learns to detect and localize objects from unannotated image data. The framework processes raw images, extracts features, and applies clustering or other unsupervised techniques to group similar objects, enabling accurate localization without manual annotations. This approach reduces dependency on labeled data, making the system more scalable and adaptable to new environments. The invention also includes methods for refining the framework's performance through iterative training and validation, ensuring robustness across diverse object types and image conditions. By eliminating the need for human annotation, this solution streamlines the deployment of object localization systems in applications such as autonomous vehicles, surveillance, and industrial automation.
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May 10, 2021
April 23, 2024
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